df <- read.csv("merged-new-version2.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df <- read.csv("merged-variety.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df <- read.csv("merged-added-functions.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df <- read.csv("merged-sim-best.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1)
df$ln_exploration <- log(df$exploration+1)
df$group = factor(df$group)
df$ln_len_unique <- log(df$len_unique+1)
df$ln_added_sum <- log(df$added_sum+1)
df$ln_sim_best <- log(df$sim.to.best+1)
df
df_new <- df[, sapply(df, is.numeric)]
cor(df_new, use = "complete.obs", method = "spearman" )
X Unnamed..0 phase novelty abs_perform_diff_best Q7_Q7_1 Q7_Q7_2 Q8_Q8_1 Q10 count total user.requirement infovis
X 1.000000000 1.000000000 0.242356186 -0.04000008 -0.039079868 0.005237315 -0.0498264942 -0.040010136 0.072013496 -0.04861136 -0.09056363 -0.09731331 -0.07128520
Unnamed..0 1.000000000 1.000000000 0.242356186 -0.04000008 -0.039079868 0.005237315 -0.0498264942 -0.040010136 0.072013496 -0.04861136 -0.09056363 -0.09731331 -0.07128520
phase 0.242356186 0.242356186 1.000000000 0.11757506 -0.092602001 -0.008916463 -0.0074969129 -0.008881488 -0.009800966 -0.13745197 0.20488584 0.16832454 0.19912172
novelty -0.040000085 -0.040000085 0.117575064 1.00000000 -0.261877799 0.070537895 0.1749363000 0.159572432 0.090598097 0.31528952 0.35310977 0.25497735 0.24719465
abs_perform_diff_best -0.039079868 -0.039079868 -0.092602001 -0.26187780 1.000000000 0.075157400 -0.1433537188 -0.132036172 -0.239114006 -0.39974589 -0.72737264 -0.58658125 -0.61780046
Q7_Q7_1 0.005237315 0.005237315 -0.008916463 0.07053789 0.075157400 1.000000000 0.5993053799 0.228181513 0.169440714 -0.04113355 -0.09257700 -0.12024975 -0.04544927
Q7_Q7_2 -0.049826494 -0.049826494 -0.007496913 0.17493630 -0.143353719 0.599305380 1.0000000000 0.304176579 0.253625610 0.02255924 0.12261038 0.05494034 0.13418964
Q8_Q8_1 -0.040010136 -0.040010136 -0.008881488 0.15957243 -0.132036172 0.228181513 0.3041765787 1.000000000 0.299848563 0.03262785 0.13638701 0.11647285 0.11014010
Q10 0.072013496 0.072013496 -0.009800966 0.09059810 -0.239114006 0.169440714 0.2536256098 0.299848563 1.000000000 0.11488257 0.21702201 0.17391984 0.17408292
count -0.048611362 -0.048611362 -0.137451974 0.31528952 -0.399745885 -0.041133553 0.0225592434 0.032627854 0.114882572 1.00000000 0.45783201 0.32531875 0.37053388
total -0.090563629 -0.090563629 0.204885844 0.35310977 -0.727372643 -0.092576997 0.1226103822 0.136387012 0.217022015 0.45783201 1.00000000 0.82479921 0.82990134
user.requirement -0.097313308 -0.097313308 0.168324538 0.25497735 -0.586581246 -0.120249752 0.0549403441 0.116472851 0.173919837 0.32531875 0.82479921 1.00000000 0.78240812
infovis -0.071285195 -0.071285195 0.199121719 0.24719465 -0.617800457 -0.045449265 0.1341896399 0.110140096 0.174082916 0.37053388 0.82990134 0.78240812 1.00000000
novelty_score 0.019692993 0.019692993 0.163234040 0.25847647 -0.603222132 -0.109564379 0.0933017185 0.116987509 0.169785938 0.37929023 0.83439087 0.52965641 0.55558433
exploration -0.136761136 -0.136761136 -0.242448405 0.29353889 -0.165601842 -0.047418598 -0.0258093480 -0.049361187 0.001496788 0.62586084 0.33055456 0.21402281 0.22567784
Group -0.968129145 -0.968129145 0.000000000 0.13234300 0.002406986 -0.003551689 0.0602397593 0.048981570 -0.070924503 0.03675437 0.16405944 0.15641934 0.13672105
len_unique -0.083893425 -0.083893425 0.188120655 0.53772703 -0.534026862 0.057679060 0.1239927882 0.253341795 0.234669806 0.43754571 0.66331157 0.45947912 0.52314195
added_sum -0.106697125 -0.106697125 -0.143686890 0.36485491 -0.284894816 -0.013155276 0.0100795268 0.050159869 0.097675586 0.66373813 0.44949580 0.30497294 0.31672078
sim.to.best -0.081539257 -0.081539257 -0.247582068 0.08055287 -0.332089485 -0.077542723 -0.0007748152 -0.088902334 -0.008770066 0.25114015 0.30757319 0.22293568 0.28889292
ln_novelty -0.040000085 -0.040000085 0.117575064 1.00000000 -0.261877799 0.070537895 0.1749363000 0.159572432 0.090598097 0.31528952 0.35310977 0.25497735 0.24719465
ln_total -0.090563629 -0.090563629 0.204885844 0.35310977 -0.727372643 -0.092576997 0.1226103822 0.136387012 0.217022015 0.45783201 1.00000000 0.82479921 0.82990134
ln_exploration -0.136761136 -0.136761136 -0.242448405 0.29353889 -0.165601842 -0.047418598 -0.0258093480 -0.049361187 0.001496788 0.62586084 0.33055456 0.21402281 0.22567784
ln_len_unique -0.083893425 -0.083893425 0.188120655 0.53772703 -0.534026862 0.057679060 0.1239927882 0.253341795 0.234669806 0.43754571 0.66331157 0.45947912 0.52314195
ln_added_sum -0.106697125 -0.106697125 -0.143686890 0.36485491 -0.284894816 -0.013155276 0.0100795268 0.050159869 0.097675586 0.66373813 0.44949580 0.30497294 0.31672078
ln_sim_best -0.081539257 -0.081539257 -0.247582068 0.08055287 -0.332089485 -0.077542723 -0.0007748152 -0.088902334 -0.008770066 0.25114015 0.30757319 0.22293568 0.28889292
novelty_score exploration Group len_unique added_sum sim.to.best ln_novelty ln_total ln_exploration ln_len_unique ln_added_sum ln_sim_best
X 0.01969299 -0.136761136 -0.968129145 -0.08389343 -0.10669712 -0.0815392574 -0.04000008 -0.09056363 -0.136761136 -0.08389343 -0.10669712 -0.0815392574
Unnamed..0 0.01969299 -0.136761136 -0.968129145 -0.08389343 -0.10669712 -0.0815392574 -0.04000008 -0.09056363 -0.136761136 -0.08389343 -0.10669712 -0.0815392574
phase 0.16323404 -0.242448405 0.000000000 0.18812066 -0.14368689 -0.2475820684 0.11757506 0.20488584 -0.242448405 0.18812066 -0.14368689 -0.2475820684
novelty 0.25847647 0.293538891 0.132342998 0.53772703 0.36485491 0.0805528689 1.00000000 0.35310977 0.293538891 0.53772703 0.36485491 0.0805528689
abs_perform_diff_best -0.60322213 -0.165601842 0.002406986 -0.53402686 -0.28489482 -0.3320894854 -0.26187780 -0.72737264 -0.165601842 -0.53402686 -0.28489482 -0.3320894854
Q7_Q7_1 -0.10956438 -0.047418598 -0.003551689 0.05767906 -0.01315528 -0.0775427227 0.07053789 -0.09257700 -0.047418598 0.05767906 -0.01315528 -0.0775427227
Q7_Q7_2 0.09330172 -0.025809348 0.060239759 0.12399279 0.01007953 -0.0007748152 0.17493630 0.12261038 -0.025809348 0.12399279 0.01007953 -0.0007748152
Q8_Q8_1 0.11698751 -0.049361187 0.048981570 0.25334180 0.05015987 -0.0889023336 0.15957243 0.13638701 -0.049361187 0.25334180 0.05015987 -0.0889023336
Q10 0.16978594 0.001496788 -0.070924503 0.23466981 0.09767559 -0.0087700658 0.09059810 0.21702201 0.001496788 0.23466981 0.09767559 -0.0087700658
count 0.37929023 0.625860839 0.036754373 0.43754571 0.66373813 0.2511401493 0.31528952 0.45783201 0.625860839 0.43754571 0.66373813 0.2511401493
total 0.83439087 0.330554560 0.164059440 0.66331157 0.44949580 0.3075731914 0.35310977 1.00000000 0.330554560 0.66331157 0.44949580 0.3075731914
user.requirement 0.52965641 0.214022810 0.156419343 0.45947912 0.30497294 0.2229356796 0.25497735 0.82479921 0.214022810 0.45947912 0.30497294 0.2229356796
infovis 0.55558433 0.225677837 0.136721048 0.52314195 0.31672078 0.2888929166 0.24719465 0.82990134 0.225677837 0.52314195 0.31672078 0.2888929166
novelty_score 1.00000000 0.257635574 0.034698153 0.52536621 0.36970115 0.1953208213 0.25847647 0.83439087 0.257635574 0.52536621 0.36970115 0.1953208213
exploration 0.25763557 1.000000000 0.100359811 0.32609569 0.89458659 0.2914102146 0.29353889 0.33055456 1.000000000 0.32609569 0.89458659 0.2914102146
Group 0.03469815 0.100359811 1.000000000 0.16517282 0.09810871 0.0306239632 0.13234300 0.16405944 0.100359811 0.16517282 0.09810871 0.0306239632
len_unique 0.52536621 0.326095688 0.165172821 1.00000000 0.53400908 0.2220856022 0.53772703 0.66331157 0.326095688 1.00000000 0.53400908 0.2220856022
added_sum 0.36970115 0.894586586 0.098108711 0.53400908 1.00000000 0.2664020253 0.36485491 0.44949580 0.894586586 0.53400908 1.00000000 0.2664020253
sim.to.best 0.19532082 0.291410215 0.030623963 0.22208560 0.26640203 1.0000000000 0.08055287 0.30757319 0.291410215 0.22208560 0.26640203 1.0000000000
ln_novelty 0.25847647 0.293538891 0.132342998 0.53772703 0.36485491 0.0805528689 1.00000000 0.35310977 0.293538891 0.53772703 0.36485491 0.0805528689
ln_total 0.83439087 0.330554560 0.164059440 0.66331157 0.44949580 0.3075731914 0.35310977 1.00000000 0.330554560 0.66331157 0.44949580 0.3075731914
ln_exploration 0.25763557 1.000000000 0.100359811 0.32609569 0.89458659 0.2914102146 0.29353889 0.33055456 1.000000000 0.32609569 0.89458659 0.2914102146
ln_len_unique 0.52536621 0.326095688 0.165172821 1.00000000 0.53400908 0.2220856022 0.53772703 0.66331157 0.326095688 1.00000000 0.53400908 0.2220856022
ln_added_sum 0.36970115 0.894586586 0.098108711 0.53400908 1.00000000 0.2664020253 0.36485491 0.44949580 0.894586586 0.53400908 1.00000000 0.2664020253
ln_sim_best 0.19532082 0.291410215 0.030623963 0.22208560 0.26640203 1.0000000000 0.08055287 0.30757319 0.291410215 0.22208560 0.26640203 1.0000000000
library(car)
Loading required package: carData
mod <- lm(ln_total~ ln_novelty + ln_len_unique, data=df)
vif(mod)
ln_novelty ln_len_unique
1.54079 1.54079
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_added_sum ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_added_sum ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-2.0925 -1.7199 -0.4125 1.3091 6.7556
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0925 0.1588 13.175 < 2e-16 ***
factor(group)0 -0.6884 0.2231 -3.085 0.00213 **
factor(group)1 -0.3726 0.2204 -1.691 0.09133 .
factor(group)2 -0.3643 0.2191 -1.663 0.09678 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.932 on 620 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.01515, Adjusted R-squared: 0.01038
F-statistic: 3.178 on 3 and 620 DF, p-value: 0.02365
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_exploration ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.2373 -0.1828 -0.1553 0.1956 0.5269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.23727 0.01951 12.162 < 2e-16 ***
factor(group)0 -0.07103 0.02723 -2.608 0.00932 **
factor(group)1 -0.04822 0.02691 -1.792 0.07363 .
factor(group)2 -0.05444 0.02676 -2.035 0.04231 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2373 on 632 degrees of freedom
Multiple R-squared: 0.01171, Adjusted R-squared: 0.007015
F-statistic: 2.495 on 3 and 632 DF, p-value: 0.05892
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_len_unique ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_len_unique ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.1090 -1.0190 0.1159 1.0643 5.0335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1090 0.1570 26.176 < 2e-16 ***
factor(group)0 -1.1892 0.2205 -5.392 9.89e-08 ***
factor(group)1 -0.3145 0.2178 -1.444 0.149
factor(group)2 -0.3315 0.2165 -1.531 0.126
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.91 on 620 degrees of freedom
(12 observations deleted due to missingness)
Multiple R-squared: 0.04969, Adjusted R-squared: 0.04509
F-statistic: 10.81 on 3 and 620 DF, p-value: 6.276e-07
tapply(df$ln_len_unique, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.135 4.007 4.109 4.691 8.514
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 1.207 3.497 2.920 4.205 7.953 4
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.303 3.961 3.794 4.997 8.415 4
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.996 3.761 3.778 4.569 8.489 4
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.7373 -0.2143 0.3493 0.8471 1.7667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1441 0.1181 43.541 < 2e-16 ***
factor(group)0 -1.0417 0.1649 -6.316 5.05e-10 ***
factor(group)1 -0.4069 0.1630 -2.497 0.012787 *
factor(group)2 -0.5990 0.1620 -3.697 0.000237 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared: 0.06155, Adjusted R-squared: 0.0571
F-statistic: 13.82 on 3 and 632 DF, p-value: 9.76e-09
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_sim_best ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_sim_best ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.08356 -0.04310 -0.01492 0.02836 0.56217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.065334 0.005936 11.007 < 2e-16 ***
factor(group)0 0.018227 0.008338 2.186 0.02919 *
factor(group)1 -0.015704 0.008231 -1.908 0.05689 .
factor(group)2 -0.022506 0.008182 -2.751 0.00612 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07123 on 604 degrees of freedom
(28 observations deleted due to missingness)
Multiple R-squared: 0.04672, Adjusted R-squared: 0.04199
F-statistic: 9.868 on 3 and 604 DF, p-value: 2.323e-06
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.52892 -0.14068 0.06865 0.15783 0.28954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.52892 0.01773 29.837 < 2e-16 ***
factor(group)0 -0.13269 0.02475 -5.362 1.16e-07 ***
factor(group)1 -0.12367 0.02445 -5.058 5.56e-07 ***
factor(group)2 -0.05178 0.02431 -2.130 0.0336 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared: 0.05844, Adjusted R-squared: 0.05397
F-statistic: 13.08 on 3 and 632 DF, p-value: 2.706e-08
df$group <- relevel(df$group, ref = "3")
mod2 <- lm(ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod2)
Call:
lm(formula = ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.7378 -0.1510 -0.1039 0.1547 0.5713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.268693 0.037650 7.137 2.72e-12 ***
factor(group)0 -0.058405 0.025897 -2.255 0.0245 *
factor(group)1 -0.040384 0.025573 -1.579 0.1148
factor(group)2 -0.049046 0.025214 -1.945 0.0522 .
Q7_Q7_1 -0.003074 0.007446 -0.413 0.6798
Q7_Q7_2 0.001945 0.007574 0.257 0.7974
Q8_Q8_1 -0.015157 0.007835 -1.935 0.0535 .
Q10 -0.014314 0.011506 -1.244 0.2139
count 0.029778 0.003028 9.834 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2212 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1499, Adjusted R-squared: 0.1388
F-statistic: 13.47 on 8 and 611 DF, p-value: < 2.2e-16
df$group <- relevel(df$group, ref = "3")
mod3 <- lm(ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod3)
Call:
lm(formula = ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.7572 -0.1521 -0.1140 0.1628 0.5513
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.225542 0.033282 6.777 2.89e-11 ***
Q7_Q7_1 -0.002899 0.007408 -0.391 0.6957
Q7_Q7_2 0.001989 0.007525 0.264 0.7916
Q8_Q8_1 -0.013677 0.007823 -1.748 0.0809 .
Q10 -0.015032 0.011307 -1.329 0.1842
count 0.030096 0.003031 9.929 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2218 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1416, Adjusted R-squared: 0.1346
F-statistic: 20.26 on 5 and 614 DF, p-value: < 2.2e-16
anova(mod2, mod3)
Analysis of Variance Table
Model 1: ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count
Model 2: ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 29.907
2 614 30.200 -3 -0.29315 1.9964 0.1133
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod4 <- lm(ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod4)
Call:
lm(formula = ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.7883 -0.0854 0.0699 0.1531 0.3014
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.343113 0.031746 10.808 < 2e-16 ***
Q7_Q7_1 -0.023135 0.007066 -3.274 0.00112 **
Q7_Q7_2 0.032111 0.007178 4.474 9.17e-06 ***
Q8_Q8_1 0.011171 0.007462 1.497 0.13490
Q10 -0.001228 0.010785 -0.114 0.90939
count 0.013646 0.002891 4.720 2.93e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2115 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.07716, Adjusted R-squared: 0.06964
F-statistic: 10.27 on 5 and 614 DF, p-value: 1.82e-09
anova(mod1, mod4)
Analysis of Variance Table
Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 26.099
2 614 27.477 -3 -1.3777 10.751 6.815e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
library(lmerTest)
Loading required package: lme4
Loading required package: Matrix
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
step
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer
Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
Data: df
AIC BIC logLik deviance df.resid
-138.4479 -111.7167 75.2239 -150.4479 630
Random effects:
Groups Name Std.Dev.
phase (Intercept) 0.005242
Residual 0.214918
Number of obs: 636, groups: phase, 4
Fixed Effects:
(Intercept) factor(group)0 factor(group)1 factor(group)2
0.52892 -0.13269 -0.12367 -0.05178
tapply(df$ln_novelty, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.4842 0.5588 0.5289 0.6162 0.6894
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.5206 0.3962 0.6073 0.6858
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.1777 0.5062 0.4053 0.6182 0.6931
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.3871 0.5465 0.4771 0.6084 0.6904
tapply(df$ln_total, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.331 4.761 5.079 5.144 5.515 5.891
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.991 4.830 4.102 5.337 5.869
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.553 5.089 4.737 5.580 5.882
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.615 4.925 4.545 5.450 5.884
tapply(df$ln_exploration, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.1379 0.2373 0.4612 0.6931
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.1662 0.3393 0.6931
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.02545 0.18906 0.40035 0.69315
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.06417 0.18283 0.35241 0.69315
tapply(df$ln_len_unique, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.135 4.007 4.109 4.691 8.514
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 1.207 3.497 2.920 4.205 7.953 4
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.303 3.961 3.794 4.997 8.415 4
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.996 3.761 3.778 4.569 8.489 4
tapply(df$ln_sim_best, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00000 0.01062 0.05968 0.06533 0.10374 0.22040 4
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00000 0.00000 0.06578 0.08356 0.12579 0.41985 8
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000000 0.002974 0.013236 0.049630 0.064522 0.611802 8
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00000 0.01304 0.03891 0.04283 0.06685 0.14108 8
library(vtree)
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")
vtree(df, "group")
vtree(df, c("phase", "group"),
fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
horiz = FALSE)
df$group <- relevel(df$group, ref = "3")
mod5 <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod5)
Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.6309 -0.2310 0.3346 0.7764 1.9667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.82832 0.22926 21.060 < 2e-16 ***
factor(group)0 -0.98353 0.15769 -6.237 8.33e-10 ***
factor(group)1 -0.42360 0.15572 -2.720 0.006709 **
factor(group)2 -0.59841 0.15354 -3.897 0.000108 ***
Q7_Q7_1 -0.19585 0.04534 -4.319 1.83e-05 ***
Q7_Q7_2 0.19627 0.04612 4.256 2.41e-05 ***
Q8_Q8_1 -0.10504 0.04771 -2.202 0.028060 *
Q10 0.17920 0.07006 2.558 0.010776 *
count 0.12749 0.01844 6.914 1.19e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.347 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1768, Adjusted R-squared: 0.166
F-statistic: 16.4 on 8 and 611 DF, p-value: < 2.2e-16
df$group <- relevel(df$group, ref = "3")
mod6 <- lm(ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod6)
Call:
lm(formula = ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count,
data = df)
Residuals:
Min 1Q Median 3Q Max
-4.5737 -0.1258 0.3665 0.7666 1.7353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.19765 0.20821 20.160 < 2e-16 ***
Q7_Q7_1 -0.18970 0.04634 -4.093 4.82e-05 ***
Q7_Q7_2 0.19885 0.04708 4.224 2.77e-05 ***
Q8_Q8_1 -0.07884 0.04894 -1.611 0.1077
Q10 0.17509 0.07073 2.475 0.0136 *
count 0.13321 0.01896 7.025 5.71e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.387 on 614 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1226, Adjusted R-squared: 0.1154
F-statistic: 17.16 on 5 and 614 DF, p-value: 6.62e-16
anova(mod5, mod6)
Analysis of Variance Table
Model 1: ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 1109
2 614 1182 -3 -73.013 13.409 1.744e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

with(df, interaction.plot(group, phase, ln_exploration, ylim=c(0, max(ln_exploration)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

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bikpKSkgIyBpbnRlcmFjdGlvbiBwbG90CmBgYAoKCgpgYGB7cn0Kd2l0aChkZiwgaW50ZXJhY3Rpb24ucGxvdChncm91cCwgcGhhc2UsIGxuX25vdmVsdHksIHlsaW09YygwLCBtYXgobG5fbm92ZWx0eSkpKSkgIyBpbnRlcmFjdGlvbiBwbG90CmBgYAoK